基于借阅记录的图书个性化推荐方法研究与应用
发布时间：2018-09-11 06:33
【摘要】：随着出版行业日益发展,高校图书馆的馆藏图书在数量和种类方面日益增多,读者在海量的图书中发现自己感兴趣的图书较为困难。目前高校图书馆的图书推荐系统一般仅依靠借阅量推荐热门图书,无法实现个性化推荐。因此有必要对高校图书个性化推荐方法进行深入研究。本文以某高校图书馆近十年的借阅记录为研究基础,设计了一种可以实现个性化图书推荐的推荐方法。方法主要包括两部分,第一部分利用协同过滤算法做推荐结果粗召回。第二部分对借阅记录进行特征提取构建读者偏好模型,利用模型对第一部分粗召回结果中的图书做评分预测,根据评分排序生成最终的推荐结果。协同过滤算法的原理在于寻找目标用户的相似用户并根据相似用户的行为为目标用户进行推荐。协同过滤算法依赖于用户-项目评分矩阵计算用户相似度。本文以高校图书馆借阅数据为研究背景,基于借阅记录生成读者-图书评分矩阵来表示读者与图书的借阅关系,用读者借阅图书的天数填充矩阵,表示读者对图书的评分,最后对矩阵中的评分归一化处理。并基于两种协同过滤算法以及两种计算相似度的方法所形成的四种算法组合做对比实验,采用平均绝对误差(Mean Absolute Error,MAE)为评价标准,选取最优的算法组合。产生的推荐结果中包含了与目标用户不同专业、不同年级的读者所借阅的图书,实现了个性化图书推荐。在方法第二部分中,针对读者信息、图书信息以及借阅信息提取特征。选取所有读者的借阅记录,按借阅时间排序,采用合适的时间窗口构建正负样本集,利用GBDT算法对数据进行训练,构建读者偏好模型,通过生成的模型预测第一部分的粗召回结果,按照评分排序产生最终的推荐结果。最后以本文所设计的推荐方法为核心建立了图书个性化推荐系统,读者通过身份认证登录web页面与推荐系统交互,获取符合自己兴趣偏好的个性化推荐结果。
[Abstract]:With the increasing development of publishing industry, the number and variety of books in university libraries are increasing day by day. It is difficult for readers to find the books they are interested in a large number of books. At present, the book recommendation system of the university library generally only depends on the quantity of borrowing to recommend the popular books, so it can not realize the individualized recommendation. Therefore, it is necessary to carry on the thorough research to the university book personalization recommendation method. Based on the borrowing records of a university library in the past ten years, this paper designs a recommendation method which can realize the personalized book recommendation. The method consists of two parts. In the first part, collaborative filtering algorithm is used to make rough recall of recommendation results. In the second part, the reader preference model is constructed by extracting the features of the borrowed records. The first part of the rough recall results of books is predicted by the model, and the final recommended results are generated according to the ranking of the books. The principle of collaborative filtering algorithm is to find similar users of target users and recommend them according to the behavior of similar users. Collaborative filtering algorithm relies on user-item scoring matrix to calculate user similarity. Based on the research background of university library borrowing data, this paper presents the relationship between readers and books by generating reader-book scoring matrix based on borrowing records, fills in the matrix with the number of days the readers borrow books, and indicates the readers' scores on books. Finally, the evaluation in the matrix is normalized. Based on two collaborative filtering algorithms and two methods to calculate the similarity of the four algorithms for comparison experiments, using the average absolute error (Mean Absolute Error,MAE) as the evaluation criteria, select the optimal combination of algorithms. The resulting recommendation results include books borrowed by readers with different specialties and grades to realize personalized book recommendation. In the second part of the method, the features of reader information, book information and borrowing information are extracted. Select all readers' borrowing records, sort according to the borrowing time, construct positive and negative sample set by appropriate time window, use GBDT algorithm to train the data, construct reader preference model. The rough recall result of the first part is predicted by the generated model, and the final recommendation result is generated according to the ranking of the score. Finally, based on the recommendation method designed in this paper, a book personalized recommendation system is established. Readers log on to the web page through identity authentication and interact with the recommendation system to obtain personalized recommendation results that accord with their interests and preferences.
【学位授予单位】：北方工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.3
本文编号：2235893
[Abstract]:With the increasing development of publishing industry, the number and variety of books in university libraries are increasing day by day. It is difficult for readers to find the books they are interested in a large number of books. At present, the book recommendation system of the university library generally only depends on the quantity of borrowing to recommend the popular books, so it can not realize the individualized recommendation. Therefore, it is necessary to carry on the thorough research to the university book personalization recommendation method. Based on the borrowing records of a university library in the past ten years, this paper designs a recommendation method which can realize the personalized book recommendation. The method consists of two parts. In the first part, collaborative filtering algorithm is used to make rough recall of recommendation results. In the second part, the reader preference model is constructed by extracting the features of the borrowed records. The first part of the rough recall results of books is predicted by the model, and the final recommended results are generated according to the ranking of the books. The principle of collaborative filtering algorithm is to find similar users of target users and recommend them according to the behavior of similar users. Collaborative filtering algorithm relies on user-item scoring matrix to calculate user similarity. Based on the research background of university library borrowing data, this paper presents the relationship between readers and books by generating reader-book scoring matrix based on borrowing records, fills in the matrix with the number of days the readers borrow books, and indicates the readers' scores on books. Finally, the evaluation in the matrix is normalized. Based on two collaborative filtering algorithms and two methods to calculate the similarity of the four algorithms for comparison experiments, using the average absolute error (Mean Absolute Error,MAE) as the evaluation criteria, select the optimal combination of algorithms. The resulting recommendation results include books borrowed by readers with different specialties and grades to realize personalized book recommendation. In the second part of the method, the features of reader information, book information and borrowing information are extracted. Select all readers' borrowing records, sort according to the borrowing time, construct positive and negative sample set by appropriate time window, use GBDT algorithm to train the data, construct reader preference model. The rough recall result of the first part is predicted by the generated model, and the final recommendation result is generated according to the ranking of the score. Finally, based on the recommendation method designed in this paper, a book personalized recommendation system is established. Readers log on to the web page through identity authentication and interact with the recommendation system to obtain personalized recommendation results that accord with their interests and preferences.
【学位授予单位】：北方工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.3
【参考文献】
中国期刊全文数据库 前10条
1 朱静薇;李红艳;;大数据时代下图书馆的挑战及其应对策略[J];现代情报;2013年05期
2 曹莹;苗启广;刘家辰;高琳;;AdaBoost算法研究进展与展望[J];自动化学报;2013年06期
3 王国霞;刘贺平;;个性化推荐系统综述[J];计算机工程与应用;2012年07期
4 鲁培;;一种改进的基于项目聚类的协同过滤推荐算法[J];科技传播;2011年01期
5 黄晓斌;张海娟;;国外数字图书馆推荐系统评述[J];情报理论与实践;2010年08期
6 吴颜;沈洁;顾天竺;陈晓红;李慧;张舒;;协同过滤推荐系统中数据稀疏问题的解决[J];计算机应用研究;2007年06期
7 周军锋,汤显,郭景峰;一种优化的协同过滤推荐算法[J];计算机研究与发展;2004年10期
8 李玮平;基于数据挖掘的图书馆读者需求分析[J];图书馆论坛;2004年03期
9 邓爱林,朱扬勇,施伯乐;基于项目评分预测的协同过滤推荐算法[J];软件学报;2003年09期
10 于金龙,李晓红,孙立新;连续属性值的整体离散化[J];哈尔滨工业大学学报;2000年03期
中国博士学位论文全文数据库 前4条
1 郁雪;基于协同过滤技术的推荐方法研究[D];天津大学;2009年
2 张亮;推荐系统中协同过滤算法若干问题的研究[D];北京邮电大学;2009年
3 孙小华;协同过滤系统的稀疏性与冷启动问题研究[D];浙江大学;2005年
4 邓爱林;电子商务推荐系统关键技术研究[D];复旦大学;2003年
，本文编号：2235893
本文链接：https://www.wllwen.com/shoufeilunwen/xixikjs/2235893.html
